Security and Compliance Material Review for Capacity Planning and Resource Scaling: Document Parsing and Chunking

What Data for This Category Consists Of

The data for this category primarily comes from cloud billing statements, server monitoring systems, business traffic forecast reports, and stress test output files. Daily processed documents include capacity planning reports, resource expansion application forms, real-time resource utilization reports, and annual budget calculation sheets. Update cycles are mostly weekly or monthly. Document structures typically include fields such as resource identifiers, usage metrics, applied quotas, budget breakdowns, and approval workflow nodes. Units include computing core counts, storage capacity, currency units, time cycles, and more. There is no unified fixed format, and some documents include visualized resource trend charts.

Constraints Imposed on Document Parsing and Chunking

Documents from different sources have significant format differences, ranging from structured tabular reports to freely formatted application documents. Parsing workflows must support multi-format input. The weekly or monthly update frequency means that document volume parsed in a single batch may be concentrated, so single-batch parsing timeouts must be avoided. Resource-related numeric fields must be extracted accurately. When chunking, the association between fields must be preserved to avoid breaking business logic after splitting. Charts attached to some documents cannot be directly extracted via text parsing, so an additional chart data extraction process must be implemented. At the same time, chunking boundaries for workflow text such as approval nodes must be handled to ensure that subsequent retrieval can fully match compliance review requirements related to approvals.

Configuration Settings

Three Common Misconfigurations

• After uploading a scanned capacity planning report, an OCR Error is displayed in the interface and parsing fails. The cause is that the high-precision mode of customPdfParse is not enabled, so tables and numeric values in scanned documents cannot be correctly identified.
• When uploading weekly resource reports in batches, FastGPT reports a file parsing timeout, but the parsing service log shows that the task has completed. The cause is that the PARSE_FILE_TIMEOUT_SECONDS configuration value is lower than the actual required parsing duration, and the processing rhythm for large-volume reports is not adapted.
• When uploading an expansion application form in simple deployment mode, the large language model does not trigger the file parsing process. The cause is that the automatic trigger rule for chunkSettingMode is not correctly configured, or the selected model does not meet the parsing trigger conditions for compliance documents.

How to Verify Correct Configuration

• Upload a scanned capacity planning report, confirm whether the parsed text includes the resource metrics and approval node content from the original document, to verify that the customPdfParse configuration is effective.
• Upload a single monthly budget report of approximately 100 MB, confirm whether the parsing time is within the interval configured by PARSE_FILE_TIMEOUT_SECONDS, and confirm that no timeout error is triggered.
• Split an expansion application form containing tables and free text, confirm whether the chunking results retain the complete paragraph of a single resource application, to verify the rationality of the chunkSize and chunkSplitter configurations.
• Enter the document parsing settings page of FastGPT v4.17.0, confirm that chunkSettingMode has been switched to manual configuration mode, and that chunking-related parameters can be customized and adjusted.

The values provided here are common starting points and should be evaluated against individual user samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.